The ability to accurately define knowledge in terms of categories has a wide range of applications. For example, the ability to accurately classify documents based on the content of the document has application in document storage and retrieval systems. For example, the Library of Congress of the United States utilizes a document classification system to store and retrieve documents. Typically, such prior art classification systems are configured in fixed hierarchical structures. For such a system, a number of high level categories or subjects are defined. Beneath each of the high level categories are additional sub categories that break the high level category into more detailed categories. The more sub categories specified in the fixed hierarchical structure, then the more detailed the classification system becomes. A detailed prior art classification system may utilize up to ten hierarchical levels. For example, the Library of Congress classifies documents based on an average of nine or ten levels of sub categories within a particular area, topic or field of study.
In modern society, there is an increasing demand for use of vast amounts of information covering a wide range of topics. In order to best utilized the vast amounts of information, an accurate and detailed classification system is required for storage and retrieval of the information. However, due to the rigid nature and limitations in detail, prior art fixed hierarchical classification systems can not adequately classify vast amounts of information that covers a wide range of information. Therefore, a classification system that accurately classifies information in a wide range of topics is desirable. Furthermore, it is desirable to classify knowledge such that the classification system is independent of language and culture so that information derived from all parts of the world may be classified under a single system.